Update app.py

app.py

@@ -4,164 +4,151 @@ import numpy as np
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
 
-Load custom fine-tuned sentiment model
+# Load custom fine-tuned sentiment model
+sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment")
+sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment")
 
-sentiment_model = AutoModelForSequenceClassification.from_pretrained(“SamanthaStorm/tether-sentiment”)
-sentiment_tokenizer = AutoTokenizer.from_pretrained(“SamanthaStorm/tether-sentiment”)
-
-Load abuse pattern model
-
-model_name = “SamanthaStorm/abuse-pattern-detector-v2”
+# Load abuse pattern model
+model_name = "SamanthaStorm/abuse-pattern-detector-v2"
 model = RobertaForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
 tokenizer = RobertaTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
 LABELS = [
-“gaslighting”, “mockery”, “dismissiveness”, “control”, “guilt_tripping”, “apology_baiting”, “blame_shifting”, “projection”,
-“contradictory_statements”, “manipulation”, “deflection”, “insults”, “obscure_formal”, “recovery_phase”, “non_abusive”,
-“suicidal_threat”, “physical_threat”, “extreme_control”
+    "gaslighting", "mockery", "dismissiveness", "control", "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
+    "contradictory_statements", "manipulation", "deflection", "insults", "obscure_formal", "recovery_phase", "non_abusive",
+    "suicidal_threat", "physical_threat", "extreme_control"
 ]
 
 THRESHOLDS = {
-“gaslighting”: 0.25, “mockery”: 0.15, “dismissiveness”: 0.30, “control”: 0.43, “guilt_tripping”: 0.19,
-“apology_baiting”: 0.45, “blame_shifting”: 0.23, “projection”: 0.50, “contradictory_statements”: 0.25,
-“manipulation”: 0.25, “deflection”: 0.30, “insults”: 0.34, “obscure_formal”: 0.25, “recovery_phase”: 0.25,
-“non_abusive”: 2.0, “suicidal_threat”: 0.45, “physical_threat”: 0.02, “extreme_control”: 0.36
+    "gaslighting": 0.25, "mockery": 0.15, "dismissiveness": 0.30, "control": 0.43, "guilt_tripping": 0.19,
+    "apology_baiting": 0.45, "blame_shifting": 0.23, "projection": 0.50, "contradictory_statements": 0.25,
+    "manipulation": 0.25, "deflection": 0.30, "insults": 0.34, "obscure_formal": 0.25, "recovery_phase": 0.25,
+    "non_abusive": 2.0, "suicidal_threat": 0.45, "physical_threat": 0.02, "extreme_control": 0.36
 }
 
 PATTERN_LABELS = LABELS[:15]
 DANGER_LABELS = LABELS[15:18]
 
 EXPLANATIONS = {
-“gaslighting”: “Gaslighting involves making someone question their own reality or perceptions…”,
-“blame_shifting”: “Blame-shifting is when one person redirects the responsibility…”,
-“projection”: “Projection involves accusing the victim of behaviors the abuser exhibits.”,
-“dismissiveness”: “Dismissiveness is belittling or disregarding another person’s feelings.”,
-“mockery”: “Mockery ridicules someone in a hurtful, humiliating way.”,
-“recovery_phase”: “Recovery phase dismisses someone’s emotional healing process.”,
-“insults”: “Insults are derogatory remarks aimed at degrading someone.”,
-“apology_baiting”: “Apology-baiting manipulates victims into apologizing for abuser’s behavior.”,
-“deflection”: “Deflection avoids accountability by redirecting blame.”,
-“control”: “Control restricts autonomy through manipulation or coercion.”,
-“extreme_control”: “Extreme control dominates decisions and behaviors entirely.”,
-“physical_threat”: “Physical threats signal risk of bodily harm.”,
-“suicidal_threat”: “Suicidal threats manipulate others using self-harm threats.”,
-“guilt_tripping”: “Guilt-tripping uses guilt to manipulate someone’s actions.”,
-“manipulation”: “Manipulation deceives to influence or control outcomes.”,
-“non_abusive”: “Non-abusive language is respectful and free of coercion.”,
-“obscure_formal”: “Obscure/formal language manipulates through confusion or superiority.”
+    "gaslighting": "Gaslighting involves making someone question their own reality or perceptions...",
+    "blame_shifting": "Blame-shifting is when one person redirects the responsibility...",
+    "projection": "Projection involves accusing the victim of behaviors the abuser exhibits.",
+    "dismissiveness": "Dismissiveness is belittling or disregarding another person’s feelings.",
+    "mockery": "Mockery ridicules someone in a hurtful, humiliating way.",
+    "recovery_phase": "Recovery phase dismisses someone's emotional healing process.",
+    "insults": "Insults are derogatory remarks aimed at degrading someone.",
+    "apology_baiting": "Apology-baiting manipulates victims into apologizing for abuser's behavior.",
+    "deflection": "Deflection avoids accountability by redirecting blame.",
+    "control": "Control restricts autonomy through manipulation or coercion.",
+    "extreme_control": "Extreme control dominates decisions and behaviors entirely.",
+    "physical_threat": "Physical threats signal risk of bodily harm.",
+    "suicidal_threat": "Suicidal threats manipulate others using self-harm threats.",
+    "guilt_tripping": "Guilt-tripping uses guilt to manipulate someone’s actions.",
+    "manipulation": "Manipulation deceives to influence or control outcomes.",
+    "non_abusive": "Non-abusive language is respectful and free of coercion.",
+    "obscure_formal": "Obscure/formal language manipulates through confusion or superiority."
 }
 
 def custom_sentiment(text):
-inputs = sentiment_tokenizer(text, return_tensors=“pt”, truncation=True, padding=True)
-with torch.no_grad():
-outputs = sentiment_model(**inputs)
-probs = torch.nn.functional.softmax(outputs.logits, dim=1)
-label_idx = torch.argmax(probs).item()
-label_map = {0: “supportive”, 1: “undermining”}
-label = label_map[label_idx]
-score = probs[0][label_idx].item()
-return {“label”: label, “score”: score}
+    inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = sentiment_model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    label_idx = torch.argmax(probs).item()
+    label_map = {0: "supportive", 1: "undermining"}
+    label = label_map[label_idx]
+    score = probs[0][label_idx].item()
+    return {"label": label, "score": score}
 
 def calculate_abuse_level(scores, thresholds):
-triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
-return round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
+    triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
+    return round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
 
 def interpret_abuse_level(score):
-if score > 80: return “Extreme / High Risk”
-elif score > 60: return “Severe / Harmful Pattern Present”
-elif score > 40: return “Likely Abuse”
-elif score > 20: return “Mild Concern”
-return “Very Low / Likely Safe”
+    if score > 80: return "Extreme / High Risk"
+    elif score > 60: return "Severe / Harmful Pattern Present"
+    elif score > 40: return "Likely Abuse"
+    elif score > 20: return "Mild Concern"
+    return "Very Low / Likely Safe"
 
 def analyze_messages(input_text, risk_flags):
-input_text = input_text.strip()
-if not input_text:
-return “Please enter a message for analysis.”
-
-sentiment = custom_sentiment(input_text)
-sentiment_label = sentiment['label']
-sentiment_score = sentiment['score']
-
-adjusted_thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()} if sentiment_label == "undermining" else THRESHOLDS.copy()
-
-inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
-with torch.no_grad():
-    outputs = model(**inputs)
-scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
-
-pattern_count = sum(score > adjusted_thresholds[label] for label, score in zip(PATTERN_LABELS, scores[:15]))
-danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
-
-contextual_flags = risk_flags if risk_flags else []
-if len(contextual_flags) >= 2:
-    danger_flag_count += 1
-
-critical_flags = ["They've threatened harm", "They monitor/follow me", "I feel unsafe when alone with them"]
-high_risk_context = any(flag in contextual_flags for flag in critical_flags)
-
-non_abusive_score = scores[LABELS.index('non_abusive')]
-if non_abusive_score > adjusted_thresholds['non_abusive']:
-    return "This message is classified as non-abusive."
-
-abuse_level = calculate_abuse_level(scores, adjusted_thresholds)
-abuse_description = interpret_abuse_level(abuse_level)
-
-if danger_flag_count >= 2:
-    resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
-else:
-    resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
-
-scored_patterns = [(label, scores[LABELS.index(label)]) for label in PATTERN_LABELS if label != "non_abusive"]
-scored_dangers = [(label, scores[LABELS.index(label)]) for label in DANGER_LABELS]
-
-top_patterns = sorted(
-    [(label, score) for label, score in scored_patterns if score > adjusted_thresholds[label]],
-    key=lambda x: x[1], reverse=True
-)[:2]
-
-top_dangers = sorted(
-    [(label, score) for label, score in scored_dangers if score > adjusted_thresholds[label]],
-    key=lambda x: x[1], reverse=True
-)
-
-highlighted_danger = top_dangers[0] if top_dangers else None
-
-top_pattern_explanations = "\n".join([
-    f"\u2022 {label.replace('_', ' ').title()}: {EXPLANATIONS.get(label, 'No explanation available.')}"
-    for label, _ in top_patterns
-])
-
-if highlighted_danger and highlighted_danger[0] not in [label for label, _ in top_patterns]:
-    danger_label = highlighted_danger[0]
-    top_pattern_explanations += f"\n\u2022 {danger_label.replace('_', ' ').title()}: {EXPLANATIONS.get(danger_label, 'No explanation available.')} (Danger Pattern)"
-
-result = (
-    f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
-    f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
-    f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
-    "Resources: " + resources + "\n\n"
-    f"Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)"
-)
-
-if contextual_flags:
-    result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag}" for flag in contextual_flags])
-if high_risk_context:
-    result += "\n\n🚨 These responses suggest a high-risk situation. Consider seeking immediate help or safety planning resources."
-
-return result
+    input_text = input_text.strip()
+    if not input_text:
+        return "Please enter a message for analysis."
+
+    sentiment = custom_sentiment(input_text)
+    sentiment_label = sentiment['label']
+    sentiment_score = sentiment['score']
+
+    adjusted_thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()} if sentiment_label == "undermining" else THRESHOLDS.copy()
+
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
+
+    pattern_count = sum(score > adjusted_thresholds[label] for label, score in zip(PATTERN_LABELS, scores[:15]))
+    danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
+
+    contextual_flags = risk_flags if risk_flags else []
+    if len(contextual_flags) >= 2:
+        danger_flag_count += 1
+
+    critical_flags = ["They've threatened harm", "They monitor/follow me", "I feel unsafe when alone with them"]
+    high_risk_context = any(flag in contextual_flags for flag in critical_flags)
+    if high_risk_context:
+        danger_flag_count += 1
+
+    non_abusive_score = scores[LABELS.index('non_abusive')]
+    if non_abusive_score > adjusted_thresholds['non_abusive']:
+        return "This message is classified as non-abusive."
+
+    abuse_level = calculate_abuse_level(scores, adjusted_thresholds)
+    abuse_description = interpret_abuse_level(abuse_level)
+
+    if danger_flag_count >= 2:
+        resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
+    else:
+        resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
+
+    scored_patterns = [
+        (label, score) for label, score in zip(PATTERN_LABELS, scores[:15])
+        if label != "non_abusive"
+    ]
+    top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2]
+
+    top_pattern_explanations = "\n".join([
+        f"\u2022 {label.replace('_', ' ').title()}: {EXPLANATIONS.get(label, 'No explanation available.')}"
+        for label, _ in top_patterns
+    ])
+
+    result = (
+        f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
+        f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
+        f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
+        "Resources: " + resources + "\n\n"
+        f"Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)"
+    )
+
+    if contextual_flags:
+        result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag}" for flag in contextual_flags])
+    if high_risk_context:
+        result += "\n\n🚨 These responses suggest a high-risk situation. Consider seeking immediate help or safety planning resources."
+
+    return result
 
 iface = gr.Interface(
-fn=analyze_messages,
-inputs=[
-gr.Textbox(lines=10, placeholder=“Enter message here…”),
-gr.CheckboxGroup(label=“Do any of these apply to your situation?”, choices=[
-“They’ve threatened harm”, “They isolate me”, “I’ve changed my behavior out of fear”,
-“They monitor/follow me”, “I feel unsafe when alone with them”
-])
-],
-outputs=[gr.Textbox(label=“Analysis Result”)],
-title=“Abuse Pattern Detector”
+    fn=analyze_messages,
+    inputs=[
+        gr.Textbox(lines=10, placeholder="Enter message here..."),
+        gr.CheckboxGroup(label="Do any of these apply to your situation?", choices=[
+            "They've threatened harm", "They isolate me", "I’ve changed my behavior out of fear",
+            "They monitor/follow me", "I feel unsafe when alone with them"
+        ])
+    ],
+    outputs=[gr.Textbox(label="Analysis Result")],
+    title="Abuse Pattern Detector"
 )
 
-if name == “main”:
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()